Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations10480
Missing cells17402
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric11
Text3
Unsupported1
Categorical2
DateTime1

Alerts

id is highly overall correlated with number_of_reviewsHigh correlation
latitude is highly overall correlated with neighbourhoodHigh correlation
longitude is highly overall correlated with neighbourhoodHigh correlation
neighbourhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with id and 2 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
room_type is highly imbalanced (63.0%)Imbalance
neighbourhood_group has 10480 (100.0%) missing valuesMissing
price has 4606 (44.0%) missing valuesMissing
last_review has 1097 (10.5%) missing valuesMissing
reviews_per_month has 1097 (10.5%) missing valuesMissing
license has 119 (1.1%) missing valuesMissing
price is highly skewed (γ1 = 31.54959784)Skewed
minimum_nights is highly skewed (γ1 = 27.30406462)Skewed
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 1097 (10.5%) zerosZeros
availability_365 has 3999 (38.2%) zerosZeros
number_of_reviews_ltm has 3771 (36.0%) zerosZeros

Reproduction

Analysis started2025-12-25 14:08:25.731988
Analysis finished2025-12-25 14:08:48.302375
Duration22.57 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct10480
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.925464 × 1017
Minimum27886
Maximum1.5062874 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:48.473543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27886
5-th percentile3382607.8
Q126293728
median6.8934743 × 1017
Q31.1196102 × 1018
95-th percentile1.4374951 × 1018
Maximum1.5062874 × 1018
Range1.5062874 × 1018
Interquartile range (IQR)1.1196102 × 1018

Descriptive statistics

Standard deviation5.6206749 × 1017
Coefficient of variation (CV)0.94856283
Kurtosis-1.6378541
Mean5.925464 × 1017
Median Absolute Deviation (MAD)6.8934743 × 1017
Skewness0.10927337
Sum-6.6665121 × 1018
Variance3.1591986 × 1035
MonotonicityStrictly increasing
2025-12-25T14:08:48.689247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
278861
 
< 0.1%
9.731820618 × 10171
 
< 0.1%
9.708935011 × 10171
 
< 0.1%
9.709002993 × 10171
 
< 0.1%
9.709097119 × 10171
 
< 0.1%
9.709397502 × 10171
 
< 0.1%
9.710403051 × 10171
 
< 0.1%
9.710960194 × 10171
 
< 0.1%
9.714036585 × 10171
 
< 0.1%
9.718410991 × 10171
 
< 0.1%
Other values (10470)10470
99.9%
ValueCountFrequency (%)
278861
< 0.1%
288711
< 0.1%
290511
< 0.1%
443911
< 0.1%
483731
< 0.1%
495521
< 0.1%
502631
< 0.1%
505151
< 0.1%
505231
< 0.1%
539211
< 0.1%
ValueCountFrequency (%)
1.506287354 × 10181
< 0.1%
1.505255614 × 10181
< 0.1%
1.5049981 × 10181
< 0.1%
1.504985778 × 10181
< 0.1%
1.503867342 × 10181
< 0.1%
1.503528945 × 10181
< 0.1%
1.50347525 × 10181
< 0.1%
1.503399985 × 10181
< 0.1%
1.503145545 × 10181
< 0.1%
1.502865938 × 10181
< 0.1%

name
Text

Distinct10186
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:49.112694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length43
Mean length37.117653
Min length1

Characters and Unicode

Total characters388993
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10009 ?
Unique (%)95.5%

Sample

1st rowRomantic, stylish B&B houseboat in canal district
2nd rowComfortable double room
3rd rowComfortable single / double room
4th rowQuiet 2-bedroom Amsterdam city centre apartment
5th rowCozy family home in Amsterdam South
ValueCountFrequency (%)
apartment3486
 
5.8%
in3274
 
5.4%
amsterdam2509
 
4.2%
2125
 
3.5%
with1623
 
2.7%
the1048
 
1.7%
spacious946
 
1.6%
garden842
 
1.4%
appartement841
 
1.4%
city821
 
1.4%
Other values (3789)42820
71.0%
2025-12-25T14:08:49.766429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50026
 
12.9%
e35249
 
9.1%
t31534
 
8.1%
a29377
 
7.6%
r24415
 
6.3%
n22967
 
5.9%
o20046
 
5.2%
i19746
 
5.1%
m16403
 
4.2%
s12940
 
3.3%
Other values (131)126290
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)388993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
50026
 
12.9%
e35249
 
9.1%
t31534
 
8.1%
a29377
 
7.6%
r24415
 
6.3%
n22967
 
5.9%
o20046
 
5.2%
i19746
 
5.1%
m16403
 
4.2%
s12940
 
3.3%
Other values (131)126290
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)388993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
50026
 
12.9%
e35249
 
9.1%
t31534
 
8.1%
a29377
 
7.6%
r24415
 
6.3%
n22967
 
5.9%
o20046
 
5.2%
i19746
 
5.1%
m16403
 
4.2%
s12940
 
3.3%
Other values (131)126290
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)388993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
50026
 
12.9%
e35249
 
9.1%
t31534
 
8.1%
a29377
 
7.6%
r24415
 
6.3%
n22967
 
5.9%
o20046
 
5.2%
i19746
 
5.1%
m16403
 
4.2%
s12940
 
3.3%
Other values (131)126290
32.5%

host_id
Real number (ℝ)

Distinct9201
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3450191 × 108
Minimum1662
Maximum7.1734696 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:50.001303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1662
5-th percentile2635611.3
Q112777805
median45478430
Q31.8771963 × 108
95-th percentile5.4900582 × 108
Maximum7.1734696 × 108
Range7.1734529 × 108
Interquartile range (IQR)1.7494182 × 108

Descriptive statistics

Standard deviation1.8043587 × 108
Coefficient of variation (CV)1.3415116
Kurtosis1.3721331
Mean1.3450191 × 108
Median Absolute Deviation (MAD)39793439
Skewness1.5704815
Sum1.40958 × 1012
Variance3.2557104 × 1016
MonotonicityNot monotonic
2025-12-25T14:08:50.217120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3911051135
 
0.3%
20373185223
 
0.2%
36430528023
 
0.2%
19840549018
 
0.2%
48898455815
 
0.1%
1457453315
 
0.1%
14309819115
 
0.1%
24164410113
 
0.1%
23715040413
 
0.1%
40889808913
 
0.1%
Other values (9191)10297
98.3%
ValueCountFrequency (%)
16621
< 0.1%
35921
< 0.1%
145891
< 0.1%
425991
< 0.1%
577221
< 0.1%
594842
< 0.1%
701631
< 0.1%
728901
< 0.1%
779501
< 0.1%
921941
< 0.1%
ValueCountFrequency (%)
7173469551
< 0.1%
7161421781
< 0.1%
7158497381
< 0.1%
7154398091
< 0.1%
7143630481
< 0.1%
7140071651
< 0.1%
7116976401
< 0.1%
7115080491
< 0.1%
7111682782
< 0.1%
7110854191
< 0.1%
Distinct3857
Distinct (%)36.8%
Missing3
Missing (%)< 0.1%
Memory size82.0 KiB
2025-12-25T14:08:50.609008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length36
Mean length6.5630429
Min length1

Characters and Unicode

Total characters68761
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2482 ?
Unique (%)23.7%

Sample

1st rowFlip
2nd rowEdwin
3rd rowEdwin
4th rowJan
5th rowVesna & Misha
ValueCountFrequency (%)
164
 
1.4%
hotel121
 
1.0%
maria75
 
0.6%
amsterdam68
 
0.6%
anna67
 
0.6%
jan62
 
0.5%
and59
 
0.5%
thomas58
 
0.5%
laura56
 
0.5%
stephan54
 
0.4%
Other values (3646)11281
93.5%
2025-12-25T14:08:51.277997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e7633
 
11.1%
a7497
 
10.9%
i5724
 
8.3%
n5554
 
8.1%
r4508
 
6.6%
o3457
 
5.0%
l3261
 
4.7%
t2711
 
3.9%
s2483
 
3.6%
u1624
 
2.4%
Other values (82)24309
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)68761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7633
 
11.1%
a7497
 
10.9%
i5724
 
8.3%
n5554
 
8.1%
r4508
 
6.6%
o3457
 
5.0%
l3261
 
4.7%
t2711
 
3.9%
s2483
 
3.6%
u1624
 
2.4%
Other values (82)24309
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)68761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7633
 
11.1%
a7497
 
10.9%
i5724
 
8.3%
n5554
 
8.1%
r4508
 
6.6%
o3457
 
5.0%
l3261
 
4.7%
t2711
 
3.9%
s2483
 
3.6%
u1624
 
2.4%
Other values (82)24309
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)68761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7633
 
11.1%
a7497
 
10.9%
i5724
 
8.3%
n5554
 
8.1%
r4508
 
6.6%
o3457
 
5.0%
l3261
 
4.7%
t2711
 
3.9%
s2483
 
3.6%
u1624
 
2.4%
Other values (82)24309
35.4%

neighbourhood_group
Unsupported

Missing  Rejected  Unsupported 

Missing10480
Missing (%)100.0%
Memory size82.0 KiB

neighbourhood
Categorical

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
De Baarsjes - Oud-West
1808 
Centrum-West
1207 
De Pijp - Rivierenbuurt
1199 
Centrum-Oost
923 
Westerpark
736 
Other values (17)
4607 

Length

Max length38
Median length23
Mean length15.588359
Min length4

Characters and Unicode

Total characters163366
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentrum-West
2nd rowCentrum-West
3rd rowCentrum-Oost
4th rowCentrum-Oost
5th rowBuitenveldert - Zuidas

Common Values

ValueCountFrequency (%)
De Baarsjes - Oud-West1808
17.3%
Centrum-West1207
11.5%
De Pijp - Rivierenbuurt1199
11.4%
Centrum-Oost923
8.8%
Westerpark736
7.0%
Zuid735
7.0%
Oud-Oost654
 
6.2%
Bos en Lommer547
 
5.2%
Oud-Noord485
 
4.6%
Oostelijk Havengebied - Indische Buurt436
 
4.2%
Other values (12)1750
16.7%

Length

2025-12-25T14:08:51.516361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4022
17.0%
de3074
13.0%
oud-west1808
 
7.7%
baarsjes1808
 
7.7%
centrum-west1207
 
5.1%
pijp1199
 
5.1%
rivierenbuurt1199
 
5.1%
centrum-oost923
 
3.9%
westerpark736
 
3.1%
zuid735
 
3.1%
Other values (27)6920
29.3%

Most occurring characters

ValueCountFrequency (%)
e21081
 
12.9%
13151
 
8.1%
r12509
 
7.7%
s11447
 
7.0%
t11305
 
6.9%
u9980
 
6.1%
-9689
 
5.9%
a6647
 
4.1%
i6309
 
3.9%
d6279
 
3.8%
Other values (31)54969
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)163366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e21081
 
12.9%
13151
 
8.1%
r12509
 
7.7%
s11447
 
7.0%
t11305
 
6.9%
u9980
 
6.1%
-9689
 
5.9%
a6647
 
4.1%
i6309
 
3.9%
d6279
 
3.8%
Other values (31)54969
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)163366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e21081
 
12.9%
13151
 
8.1%
r12509
 
7.7%
s11447
 
7.0%
t11305
 
6.9%
u9980
 
6.1%
-9689
 
5.9%
a6647
 
4.1%
i6309
 
3.9%
d6279
 
3.8%
Other values (31)54969
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)163366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e21081
 
12.9%
13151
 
8.1%
r12509
 
7.7%
s11447
 
7.0%
t11305
 
6.9%
u9980
 
6.1%
-9689
 
5.9%
a6647
 
4.1%
i6309
 
3.9%
d6279
 
3.8%
Other values (31)54969
33.6%

latitude
Real number (ℝ)

High correlation 

Distinct7583
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.366679
Minimum52.290276
Maximum52.42512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:51.721232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52.290276
5-th percentile52.342858
Q152.355694
median52.36569
Q352.37651
95-th percentile52.396064
Maximum52.42512
Range0.13484378
Interquartile range (IQR)0.020816359

Descriptive statistics

Standard deviation0.017246466
Coefficient of variation (CV)0.00032934046
Kurtosis1.8918496
Mean52.366679
Median Absolute Deviation (MAD)0.010406802
Skewness0.0079005003
Sum548802.8
Variance0.0002974406
MonotonicityNot monotonic
2025-12-25T14:08:51.965354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.3650398313
 
0.1%
52.388112
 
0.1%
52.367502311
 
0.1%
52.339954210
 
0.1%
52.367923110
 
0.1%
52.3637910
 
0.1%
52.3727210
 
0.1%
52.374259210
 
0.1%
52.364310
 
0.1%
52.354559
 
0.1%
Other values (7573)10375
99.0%
ValueCountFrequency (%)
52.290276221
 
< 0.1%
52.291221
 
< 0.1%
52.291251
 
< 0.1%
52.291321
 
< 0.1%
52.291581
 
< 0.1%
52.291661
 
< 0.1%
52.291891
 
< 0.1%
52.29214521
 
< 0.1%
52.29246781
 
< 0.1%
52.292476
0.1%
ValueCountFrequency (%)
52.425121
< 0.1%
52.424761
< 0.1%
52.424731
< 0.1%
52.424671
< 0.1%
52.424611
< 0.1%
52.42371
< 0.1%
52.423446691
< 0.1%
52.423211
< 0.1%
52.4231
< 0.1%
52.4226732
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct8616
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8894471
Minimum4.75587
Maximum5.02815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:52.187067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.75587
5-th percentile4.8444293
Q14.8646175
median4.8875165
Q34.9086747
95-th percentile4.9456944
Maximum5.02815
Range0.27228
Interquartile range (IQR)0.044057224

Descriptive statistics

Standard deviation0.034821211
Coefficient of variation (CV)0.0071217074
Kurtosis1.1275532
Mean4.8894471
Median Absolute Deviation (MAD)0.021913535
Skewness0.49770836
Sum51241.405
Variance0.0012125168
MonotonicityNot monotonic
2025-12-25T14:08:52.425414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9143813
 
0.1%
4.90997505213
 
0.1%
4.888921911
 
0.1%
4.924154310
 
0.1%
4.896362410
 
0.1%
4.899257710
 
0.1%
4.8886710
 
0.1%
4.867979
 
0.1%
4.91218
 
0.1%
4.89611038
 
0.1%
Other values (8606)10378
99.0%
ValueCountFrequency (%)
4.755871
< 0.1%
4.7566566991
< 0.1%
4.771451
< 0.1%
4.773731
< 0.1%
4.774831
< 0.1%
4.7778669241
< 0.1%
4.777991
< 0.1%
4.7779999521
< 0.1%
4.77811
< 0.1%
4.778471
< 0.1%
ValueCountFrequency (%)
5.028151
< 0.1%
5.0266686381
< 0.1%
5.024851
< 0.1%
5.018891
< 0.1%
5.018391
< 0.1%
5.018131
< 0.1%
5.017121
< 0.1%
5.016671
< 0.1%
5.0164122581
< 0.1%
5.0163772041
< 0.1%

room_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
Entire home/apt
8561 
Private room
1839 
Hotel room
 
49
Shared room
 
31

Length

Max length15
Median length15
Mean length14.438359
Min length10

Characters and Unicode

Total characters151314
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt8561
81.7%
Private room1839
 
17.5%
Hotel room49
 
0.5%
Shared room31
 
0.3%

Length

2025-12-25T14:08:52.625864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-25T14:08:52.780811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
entire8561
40.8%
home/apt8561
40.8%
room1919
 
9.2%
private1839
 
8.8%
hotel49
 
0.2%
shared31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e19041
12.6%
t19010
12.6%
o12448
8.2%
r12350
8.2%
m10480
 
6.9%
10480
 
6.9%
a10431
 
6.9%
i10400
 
6.9%
h8592
 
5.7%
p8561
 
5.7%
Other values (9)29521
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)151314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e19041
12.6%
t19010
12.6%
o12448
8.2%
r12350
8.2%
m10480
 
6.9%
10480
 
6.9%
a10431
 
6.9%
i10400
 
6.9%
h8592
 
5.7%
p8561
 
5.7%
Other values (9)29521
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)151314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e19041
12.6%
t19010
12.6%
o12448
8.2%
r12350
8.2%
m10480
 
6.9%
10480
 
6.9%
a10431
 
6.9%
i10400
 
6.9%
h8592
 
5.7%
p8561
 
5.7%
Other values (9)29521
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)151314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e19041
12.6%
t19010
12.6%
o12448
8.2%
r12350
8.2%
m10480
 
6.9%
10480
 
6.9%
a10431
 
6.9%
i10400
 
6.9%
h8592
 
5.7%
p8561
 
5.7%
Other values (9)29521
19.5%

price
Real number (ℝ)

Missing  Skewed 

Distinct663
Distinct (%)11.3%
Missing4606
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean336.78515
Minimum35
Maximum80018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:52.973053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile93
Q1161
median222
Q3314
95-th percentile550
Maximum80018
Range79983
Interquartile range (IQR)153

Descriptive statistics

Standard deviation1985.6619
Coefficient of variation (CV)5.8959305
Kurtosis1096.4667
Mean336.78515
Median Absolute Deviation (MAD)72
Skewness31.549598
Sum1978276
Variance3942853.1
MonotonicityNot monotonic
2025-12-25T14:08:53.199981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225100
 
1.0%
18096
 
0.9%
20091
 
0.9%
25067
 
0.6%
27066
 
0.6%
30064
 
0.6%
16255
 
0.5%
19048
 
0.5%
31546
 
0.4%
19842
 
0.4%
Other values (653)5199
49.6%
(Missing)4606
44.0%
ValueCountFrequency (%)
351
 
< 0.1%
391
 
< 0.1%
431
 
< 0.1%
461
 
< 0.1%
492
 
< 0.1%
501
 
< 0.1%
511
 
< 0.1%
533
< 0.1%
564
< 0.1%
575
< 0.1%
ValueCountFrequency (%)
800182
< 0.1%
500002
< 0.1%
400003
< 0.1%
139781
 
< 0.1%
110001
 
< 0.1%
100001
 
< 0.1%
99991
 
< 0.1%
64741
 
< 0.1%
52002
< 0.1%
45001
 
< 0.1%

minimum_nights
Real number (ℝ)

Skewed 

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3902672
Minimum1
Maximum1001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:53.435409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum1001
Range1000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation19.80735
Coefficient of variation (CV)4.5116503
Kurtosis1009.9475
Mean4.3902672
Median Absolute Deviation (MAD)1
Skewness27.304065
Sum46010
Variance392.33112
MonotonicityNot monotonic
2025-12-25T14:08:53.653396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32961
28.3%
22958
28.2%
11822
17.4%
41109
 
10.6%
5731
 
7.0%
7294
 
2.8%
6179
 
1.7%
1465
 
0.6%
1063
 
0.6%
3043
 
0.4%
Other values (45)255
 
2.4%
ValueCountFrequency (%)
11822
17.4%
22958
28.2%
32961
28.3%
41109
 
10.6%
5731
 
7.0%
6179
 
1.7%
7294
 
2.8%
827
 
0.3%
913
 
0.1%
1063
 
0.6%
ValueCountFrequency (%)
10011
 
< 0.1%
8001
 
< 0.1%
3653
< 0.1%
3644
< 0.1%
3635
< 0.1%
3601
 
< 0.1%
3002
 
< 0.1%
2991
 
< 0.1%
2101
 
< 0.1%
1802
 
< 0.1%

number_of_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct573
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.813359
Minimum0
Maximum5097
Zeros1097
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:53.857723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q330
95-th percentile264
Maximum5097
Range5097
Interquartile range (IQR)27

Descriptive statistics

Standard deviation131.50744
Coefficient of variation (CV)2.750433
Kurtosis312.19347
Mean47.813359
Median Absolute Deviation (MAD)9
Skewness11.734389
Sum501084
Variance17294.207
MonotonicityNot monotonic
2025-12-25T14:08:54.074142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01097
 
10.5%
1641
 
6.1%
2605
 
5.8%
3541
 
5.2%
4495
 
4.7%
5450
 
4.3%
6393
 
3.8%
7355
 
3.4%
8293
 
2.8%
9282
 
2.7%
Other values (563)5328
50.8%
ValueCountFrequency (%)
01097
10.5%
1641
6.1%
2605
5.8%
3541
5.2%
4495
4.7%
5450
4.3%
6393
 
3.8%
7355
 
3.4%
8293
 
2.8%
9282
 
2.7%
ValueCountFrequency (%)
50971
< 0.1%
37261
< 0.1%
31871
< 0.1%
18791
< 0.1%
14771
< 0.1%
14451
< 0.1%
11331
< 0.1%
10951
< 0.1%
10801
< 0.1%
10651
< 0.1%

last_review
Date

Missing 

Distinct1403
Distinct (%)15.0%
Missing1097
Missing (%)10.5%
Memory size82.0 KiB
Minimum2014-01-04 00:00:00
Maximum2025-09-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-25T14:08:54.280036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:54.505715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation  Missing 

Distinct687
Distinct (%)7.3%
Missing1097
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean0.9986678
Minimum0.01
Maximum99.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:54.728464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.06
Q10.2
median0.41
Q30.91
95-th percentile4.02
Maximum99.42
Range99.41
Interquartile range (IQR)0.71

Descriptive statistics

Standard deviation2.3061429
Coefficient of variation (CV)2.3092193
Kurtosis521.3215
Mean0.9986678
Median Absolute Deviation (MAD)0.26
Skewness16.982169
Sum9370.5
Variance5.3182952
MonotonicityNot monotonic
2025-12-25T14:08:55.325543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08166
 
1.6%
0.24166
 
1.6%
0.12161
 
1.5%
0.15149
 
1.4%
0.14146
 
1.4%
0.13145
 
1.4%
0.11143
 
1.4%
0.19133
 
1.3%
0.18133
 
1.3%
0.17131
 
1.2%
Other values (677)7910
75.5%
(Missing)1097
 
10.5%
ValueCountFrequency (%)
0.0127
 
0.3%
0.0249
 
0.5%
0.0396
0.9%
0.04107
1.0%
0.0595
0.9%
0.06126
1.2%
0.07127
1.2%
0.08166
1.6%
0.09130
1.2%
0.1116
1.1%
ValueCountFrequency (%)
99.421
< 0.1%
67.871
< 0.1%
51.911
< 0.1%
47.051
< 0.1%
44.821
< 0.1%
42.521
< 0.1%
36.61
< 0.1%
36.51
< 0.1%
33.331
< 0.1%
28.091
< 0.1%
Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.844084
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:55.499573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum35
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1590959
Coefficient of variation (CV)1.7130976
Kurtosis50.72472
Mean1.844084
Median Absolute Deviation (MAD)0
Skewness6.3606455
Sum19326
Variance9.9798867
MonotonicityNot monotonic
2025-12-25T14:08:55.670264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18623
82.3%
2750
 
7.2%
3231
 
2.2%
4156
 
1.5%
5125
 
1.2%
6108
 
1.0%
770
 
0.7%
954
 
0.5%
1352
 
0.5%
1050
 
0.5%
Other values (7)261
 
2.5%
ValueCountFrequency (%)
18623
82.3%
2750
 
7.2%
3231
 
2.2%
4156
 
1.5%
5125
 
1.2%
6108
 
1.0%
770
 
0.7%
848
 
0.5%
954
 
0.5%
1050
 
0.5%
ValueCountFrequency (%)
3535
0.3%
2346
0.4%
1818
 
0.2%
1545
0.4%
1352
0.5%
1236
0.3%
1133
0.3%
1050
0.5%
954
0.5%
848
0.5%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.999809
Minimum0
Maximum365
Zeros3999
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:55.869583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3173
95-th percentile347
Maximum365
Range365
Interquartile range (IQR)173

Descriptive statistics

Standard deviation122.27616
Coefficient of variation (CV)1.3008128
Kurtosis-0.49198934
Mean93.999809
Median Absolute Deviation (MAD)20
Skewness1.0189258
Sum985118
Variance14951.459
MonotonicityNot monotonic
2025-12-25T14:08:56.092840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03999
38.2%
2108
 
1.0%
3103
 
1.0%
1101
 
1.0%
897
 
0.9%
25394
 
0.9%
482
 
0.8%
580
 
0.8%
977
 
0.7%
1067
 
0.6%
Other values (356)5672
54.1%
ValueCountFrequency (%)
03999
38.2%
1101
 
1.0%
2108
 
1.0%
3103
 
1.0%
482
 
0.8%
580
 
0.8%
655
 
0.5%
766
 
0.6%
897
 
0.9%
977
 
0.7%
ValueCountFrequency (%)
36547
0.4%
36447
0.4%
36339
0.4%
36244
0.4%
36124
0.2%
36016
 
0.2%
35921
0.2%
35850
0.5%
35716
 
0.2%
35625
0.2%

number_of_reviews_ltm
Real number (ℝ)

High correlation  Zeros 

Distinct149
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5880725
Minimum0
Maximum949
Zeros3771
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-12-25T14:08:56.304797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile47
Maximum949
Range949
Interquartile range (IQR)6

Descriptive statistics

Standard deviation25.195305
Coefficient of variation (CV)2.9337555
Kurtosis406.84532
Mean8.5880725
Median Absolute Deviation (MAD)2
Skewness15.012953
Sum90003
Variance634.80339
MonotonicityNot monotonic
2025-12-25T14:08:56.528390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03771
36.0%
11082
 
10.3%
2947
 
9.0%
3763
 
7.3%
4614
 
5.9%
5470
 
4.5%
6390
 
3.7%
7281
 
2.7%
8229
 
2.2%
9156
 
1.5%
Other values (139)1777
17.0%
ValueCountFrequency (%)
03771
36.0%
11082
 
10.3%
2947
 
9.0%
3763
 
7.3%
4614
 
5.9%
5470
 
4.5%
6390
 
3.7%
7281
 
2.7%
8229
 
2.2%
9156
 
1.5%
ValueCountFrequency (%)
9491
< 0.1%
8131
< 0.1%
6651
< 0.1%
5981
< 0.1%
5521
< 0.1%
4851
< 0.1%
3741
< 0.1%
3591
< 0.1%
3251
< 0.1%
2751
< 0.1%

license
Text

Missing 

Distinct9080
Distinct (%)87.6%
Missing119
Missing (%)1.1%
Memory size82.0 KiB
2025-12-25T14:08:57.010093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length22.246115
Min length6

Characters and Unicode

Total characters230492
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8702 ?
Unique (%)84.0%

Sample

1st row0363 974D 4986 7411 88D8
2nd row0363 607B EA74 0BD8 2F6F
3rd row0363 607B EA74 0BD8 2F6F
4th row0363 E76E F06A C1DD 172C
5th row0363 4A2B A6AD 0196 F684
ValueCountFrequency (%)
03638474
 
19.0%
exempt778
 
1.7%
abcd21
 
< 0.1%
ab1218
 
< 0.1%
123417
 
< 0.1%
000016
 
< 0.1%
790e16
 
< 0.1%
78ad15
 
< 0.1%
887515
 
< 0.1%
3c0515
 
< 0.1%
Other values (26590)35140
78.9%
2025-12-25T14:08:57.704652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34164
14.8%
328732
12.5%
019338
 
8.4%
618949
 
8.2%
E10239
 
4.4%
99836
 
4.3%
19711
 
4.2%
C9627
 
4.2%
89611
 
4.2%
59581
 
4.2%
Other values (43)70704
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)230492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34164
14.8%
328732
12.5%
019338
 
8.4%
618949
 
8.2%
E10239
 
4.4%
99836
 
4.3%
19711
 
4.2%
C9627
 
4.2%
89611
 
4.2%
59581
 
4.2%
Other values (43)70704
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)230492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34164
14.8%
328732
12.5%
019338
 
8.4%
618949
 
8.2%
E10239
 
4.4%
99836
 
4.3%
19711
 
4.2%
C9627
 
4.2%
89611
 
4.2%
59581
 
4.2%
Other values (43)70704
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)230492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34164
14.8%
328732
12.5%
019338
 
8.4%
618949
 
8.2%
E10239
 
4.4%
99836
 
4.3%
19711
 
4.2%
C9627
 
4.2%
89611
 
4.2%
59581
 
4.2%
Other values (43)70704
30.7%

Interactions

2025-12-25T14:08:45.440892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:27.351702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.289785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.084831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.791040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.686479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.455627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.187391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.156022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.896252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.670832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:45.594404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:27.547553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.453029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.242715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.954102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.854023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.611605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.346205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.314633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.066072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.859592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:45.751293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:27.712791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.610601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.406634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:33.111788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.018567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.763044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.505497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.479340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.229765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.032248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.218809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:27.874985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.779332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.559645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:33.486202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.183311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.920075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.662341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.665484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.391809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.192130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.359495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.028588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.949201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.709509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:33.629146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.342636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.069201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.818131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.829909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.541453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.345539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.514536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.199975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.118192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:31.881384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:33.795479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.513626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.244622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:39.249022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.999498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.711116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.517742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.654054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.347973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.270939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.033199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:33.944066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.669891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.398637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:39.396052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.144593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:42.866822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.672313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.801281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.499656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.426797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.183791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.090927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.819593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.556042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:39.534200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.296622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.025028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.823834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:46.942677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.647840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.578545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.333960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.231836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:35.977378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.705522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:39.682484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.439604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.182932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:44.977573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:47.097239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:28.977720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.779060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.493543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.386739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.145312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:37.883468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:39.844876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.593567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.346815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:45.140903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:47.246322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:29.137908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:30.941528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:32.649282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:34.537908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:36.309341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:38.050596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:40.008941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:41.755836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:43.516577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-25T14:08:45.297363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-12-25T14:08:57.896268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhoodnumber_of_reviewsnumber_of_reviews_ltmpricereviews_per_monthroom_type
availability_3651.0000.2690.1490.171-0.0080.029-0.1840.0640.1000.3730.1200.4080.171
calculated_host_listings_count0.2691.0000.2220.0400.0370.059-0.2890.1160.1500.198-0.1440.2730.266
host_id0.1490.2221.0000.362-0.0470.016-0.2250.066-0.1380.0610.0140.1420.141
id0.1710.0400.3621.000-0.019-0.018-0.1110.045-0.635-0.0440.1020.1330.114
latitude-0.0080.037-0.047-0.0191.000-0.077-0.0270.6820.0600.064-0.0680.0740.109
longitude0.0290.0590.016-0.018-0.0771.0000.0010.6690.0320.024-0.0360.0400.093
minimum_nights-0.184-0.289-0.225-0.111-0.0270.0011.0000.051-0.147-0.2390.081-0.3440.000
neighbourhood0.0640.1160.0660.0450.6820.6690.0511.0000.0750.0560.1330.0610.177
number_of_reviews0.1000.150-0.138-0.6350.0600.032-0.1470.0751.0000.613-0.2130.5900.148
number_of_reviews_ltm0.3730.1980.061-0.0440.0640.024-0.2390.0560.6131.000-0.2290.7880.129
price0.120-0.1440.0140.102-0.068-0.0360.0810.133-0.213-0.2291.000-0.3010.207
reviews_per_month0.4080.2730.1420.1330.0740.040-0.3440.0610.5900.788-0.3011.0000.125
room_type0.1710.2660.1410.1140.1090.0930.0000.1770.1480.1290.2070.1251.000

Missing values

2025-12-25T14:08:47.460788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-25T14:08:47.844730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-25T14:08:48.182995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
027886Romantic, stylish B&B houseboat in canal district97647FlipNaNCentrum-West52.3876104.891880Private room132.033112025-09-071.87117330363 974D 4986 7411 88D8
128871Comfortable double room124245EdwinNaNCentrum-West52.3677504.890920Private room89.027322025-09-073.992126930363 607B EA74 0BD8 2F6F
229051Comfortable single / double room124245EdwinNaNCentrum-Oost52.3658404.891110Private room61.028492025-09-084.81295860363 607B EA74 0BD8 2F6F
344391Quiet 2-bedroom Amsterdam city centre apartment194779JanNaNCentrum-Oost52.3716804.914710Entire home/aptNaN3422022-08-200.231000363 E76E F06A C1DD 172C
448373Cozy family home in Amsterdam South220434Vesna & MishaNaNBuitenveldert - Zuidas52.3278084.876800Entire home/aptNaN352024-04-280.191000363 4A2B A6AD 0196 F684
549552Multatuli Luxury Guest Suite in top location225987Joanna & MPNaNCentrum-West52.3802804.890890Entire home/apt322.036092025-08-263.361223530363 576A D827 5085 6B83
650263Central de Lux 2 bedrooms (4p) apt 125 sqm230246DonaldNaNCentrum-Oost52.3693784.929579Entire home/apt457.021772025-09-010.971354110363 7F3D 0BAE 28C8 C7D2
750515Family Home (No drugs, smoking or parties)231864KarinNaNBos en Lommer52.3755904.838570Entire home/apt198.07202025-08-230.15124430363 5DDB E495 A6D5 CEC6
850523B & B de 9 Straatjes (city center)231946RaymondNaNCentrum-West52.3695904.884230Entire home/apt162.025632025-08-243.151261790363 22DC 0E52 B70B 0FB8
953921Amsterdam Stylish Lakeview Apartment252245IngridNaNIJburg - Zeeburgereiland52.3555905.003200Entire home/aptNaN1122024-05-250.141000363 B43C B1D4 2666 3739
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
104701502865938487978016Cosy one bedroom apartment in Amsterdam Noord102449031DiosaNaNOud-Noord52.3990304.915410Entire home/apt137.010NaNNaN11700363 2AB1 E13E D42F 386D
104711503145545400026458Downtown apartment w/terrasse and view to a canal22534485HamiltonNaNCentrum-Oost52.3618274.905296Private room87.010NaNNaN1870NaN
104721503399985423712696The Staal House420845707HankNaNCentrum-Oost52.3681564.898271Entire home/apt500.010NaNNaN17000363 7302 7B95 4CF8 8169
104731503475250056511603Comfortable spacious apartment in great location175885486ChristianNaNDe Baarsjes - Oud-West52.3686524.855999Entire home/apt149.020NaNNaN129100363 AA29 4900 1AD4 76A9
104741503528945274992542Pearl of the Cuyp | Modern Comfort in De Pijp717346955DennisNaNDe Pijp - Rivierenbuurt52.3542304.895250Entire home/apt423.020NaNNaN135200363 033B 7FC4 5C3A DB9F
104751503867342263201504test host, don't book78127165KaiyingNaNCentrum-Oost52.3599004.905820Entire home/apt6474.010NaNNaN13650NaN
104761504985777531398085Bright studio with canal view613779SilvanaNaNBos en Lommer52.3779304.846690Entire home/apt130.010NaNNaN417600363F7E548AEB29F3BA3
104771504998100462399057Bright & Spacious Luxury Corner Apartment715849738JasonNaNWesterpark52.3737804.871858Entire home/apt499.050NaNNaN12000363 5876 BBB2 EF1F 097D
104781505255613607359391Bright and Spacious Ground Floor App. with Garden31681093JeromNaNOud-Noord52.3865504.917210Entire home/apt144.010NaNNaN12700363 3146 D0B7 73A7 E9FA
104791506287353709120640Stylish & cozy apartment in Amsterdam West32442604LisaNaNBos en Lommer52.3762994.860642Entire home/apt152.020NaNNaN12600363 044C 2949 EABC E0B8